Predictive analytics
Demand and sales forecasting, churn prediction, lead and credit scoring built on your historical records.
AI & Machine Learning
AI & machine learning development
Models trained on your data, built to run in production.
Most companies already hold the data to predict their next quarter, their riskiest accounts or their busiest week. We turn that history into models that answer those questions every day.
Machine learning that earns its place in your business.
Machine learning finds patterns in historical data and uses them to make predictions about new cases: which customer is about to leave, how much stock you will need next month, which transaction looks like fraud, which scan needs a doctor’s attention first. Off-the-shelf tools cover generic problems; a custom model is worth it when the problem, the data or the accuracy you need is specific to your business.
We start from the decision the model should improve and the data you already have, not from an algorithm. Then we build the simplest model that meets the target, prove it on held-out data, and wire it into your product or workflow so the prediction reaches the person who acts on it.
AI & Machine Learning Development
What we offer
Classification & detection
Models that sort documents, tickets, transactions or images into the categories your team works with.
Anomaly & fraud detection
Spotting unusual transactions, sensor readings or behaviour early, with alerts your team can act on.
Recommendation systems
Personalised product, content and next-best-action suggestions based on behaviour and purchase history.
Optimization
Pricing, scheduling, routing and resource allocation models that find better answers than rules of thumb.
Model audit & improvement
Reviewing an existing model for accuracy, bias and drift, then retraining or rebuilding what does not hold up.
When it’s the right fit
- You have months or years of data and suspect it can predict something valuable
- A manual review or decision takes your team hours and follows recognisable patterns
- Rule-based logic has grown unmanageable and still misses edge cases
- You have a model in a notebook that never made it into production
How we deliver
- 01Data auditWe check what your data can actually support — volume, quality, labels and leakage — before anyone promises accuracy.
- 02Baseline & prototypeA simple baseline, then a working model in 3–6 weeks, measured against the metric agreed at the start.
- 03IntegrationAn API or batch job that puts predictions where decisions are made: your app, CRM, dashboard or back office.
- 04Monitoring & retrainingAccuracy and data-drift tracking in production, with a retraining routine so the model stays useful.
Technology
Tools we use for AI & Machine Learning Development
- Python
- scikit-learn
- PyTorch
- TensorFlow
- XGBoost
- Pandas
- FastAPI
- MLflow
Frequently asked questions
How much data do we need for a custom machine learning model?
It depends on the problem, but many useful models start from a few thousand labelled examples. We check your data in the audit phase and tell you plainly if it is not enough — and what it would take to collect more.
How long does it take to build a machine learning model?
A working prototype usually takes 3–6 weeks. Taking it to production with integration and monitoring typically takes 3–4 months from kickoff.
Who owns the model and the code?
You do. The code, the trained model weights and the data pipelines are handed over to you in full.
Next step
Tell us what you want to build.
A free 30-minute consultation with an engineer — no obligation, reply within one business day.
Contact us